摘要
近年来颜色指数与激光雷达(LiDAR)点云数据被广泛应用于农业和林业遥感中,但同时带来了异物同谱与数据冗余的问题。以喀斯特高原峡谷区火龙果植株为例,利用无人机可见光影像和影像匹配点云数据,设置方法实验区和精度验证区。通过融合可见光波段差异颜色指数(VDVI)、红绿蓝颜色指数(RGBVI)、归一化绿蓝差异指数(NGBDI)、归一化绿红差异颜色指数(NGRDI)4种颜色指数计算结果和冠层高度模型(CHM)数据,构建融合颜色指数与点云数据空间结构的火龙果单株识别规则进行分割提取,以真实火龙果植株轮廓为参考数据对植株提取精度进行评价,将4种颜色指数融合提取的精度分别与单一因子颜色指数和CHM分割提取精度进行比较分析,得到最优识别提取方案并验证方法的可行性。实验结果表明:融合颜色指数与空间结构的方法提取精度更高,F测度都超91%,匹配面积值与绘制真实值平均值相差约0.1 m^(2);VDVI指数融合提取结果精度最高,单株面积值最接近真实值,均方根误差(RMSE)达0.28 m^(2),且面积值数据整体集中不分散;精度验证区F测度达88.12%,RMSE为0.27m^(2),火龙果植株整体提取效果较好,低矮灌木在一定程度上会影响火龙果植株的识别精度。所提融合影像光谱特征与点云数据空间结构的方法有效增强了植株识别特征,对喀斯特山地火龙果植株识别具有较好适应性,可为喀斯特山地火龙果单株提取提供一定参考。
Recently, color index and light detection and ranging(LiDAR) point cloud data have been extensively used in agriculture and forestry remote sensing. However, they bring the characteristics of different objects in the same spectrum and data redundancy. Considering pitaya plants in the Karst Plateau Valley area as an example, the method test area and accuracy verification area were set using UAV visible light images and image matching point cloud data. By fusing the calculation results of four color indexes of visible band difference color index(VDVI), red green blue color index(RGBVI), normalized green blue difference index(NGBDI), normalized green red difference color index(NGRDI) and canopy height model(CHM) data, the identification rules of pitaya single plant that fuse color index and spatial structure of point cloud data were developed for segmentation and extraction. The accuracy evaluation data of real pitaya plant contour was established as a reference. The precision of fusion extraction of four color indexes and point cloud data was compared with a single factor of color index or CHM segmentation. Then,the optimal extraction scheme is selected to confirm the feasibility of the proposed method. The results are as follows. The fusion method of the color index and spatial structure has higher extraction accuracy. The F measures are >91%, and the difference between the matching area and mean values of the real value is ~0. 1 m^(2). The VDVI index fusion results achieved the highest accuracy. The area value per plant was the closest to the true value;the root mean square error(RMSE) was 0. 28 m^(2), and the area value data were concentrated. The F measure in the accuracy verification area was 88. 12%, and the RMSE was 0. 27 m^(2). The overall extraction effect of pitaya plants was good;however, low shrubs could affect the accuracy of pitaya plants identification to certain extent. The proposed method of fusion image spectral features and spatial structure of point cloud data can effectively enhance plant recog
作者
肖冬娜
周忠发
尹林江
黄登红
张扬
黎前霞
Xiao Dongna;Zhou Zhongfa;Yin Linjiang;Huang Denghong;Zhang Yang;Li Qianxia(School of Karst Science/School of Geography&Environmental Science,Guizhou Normal University,Guiyang 550001,Guizhou,China;State Engineering Technology Institute For for Karst Desertification Control,Guizhou Normal University,Guiyang 550001,Guizhou,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第10期479-493,共15页
Laser & Optoelectronics Progress
基金
贵州省高层次创新型人才培养计划“百”层次人才(黔科合平台人才[2016]5674)
贵州省科学技术基金(黔科合基础-ZK[2021]一般194)
2019年度贵州省农业重大产业科学研究攻关项目(黔教合KY字[2019]032)
贵州省研究生教育创新计划(黔教合YJSCXJH[2020]103)。
关键词
遥感
空间结构特征
颜色指数
冠层高度模型
影像匹配点云
喀斯特山地
remote sensing
spatial structure characteristic
color index
canopy height model
image matching point cloud
Karst mountain
